Despite achieving marginally lower F1 scores, the pointer network-based model can be considered more intuitive and suitable for this task. WordDecoding may not extract the special tokens and relation tokens at the right time steps, which is critical for finding the tuples from the generated sequence of words. PtrNetDecoding always extracts two entities of varying length and a relation for every tuple. We also observe that PtrNetDecoding is more than two times faster and takes one-third of the GPU memory of WordDecoding during training and inference. This speedup and smaller memory consumption are achieved due to the fewer number of decoding steps of PtrNetDecoding compared to WordDecoding. PtrNetDecoding extracts an entire tuple at each time step, whereas WordDecoding extracts just one word at each time step and so requires eight time steps on average to extract a tuple (assuming that the average length of an entity is two). The softmax operation at the projection layer of WordDecoding is applied across the entire vocabulary and the vocabulary size can be large (more than 40,000 for our datasets). In case of PtrNetDecoding, the softmax operation is applied across the sentence length (maximum of 100 in our experiments) and across the relation set (24 and 29 for our datasets). The costly softmax operation and the higher number of decoding time steps significantly increase the training and inference time for WordDecoding. The encoder-decoder model proposed by [1]} faces a similar softmax-related problem as their target vocabulary contains the entire Wikidata entity IDs and relation IDs which is in the millions. HRL, which uses a deep reinforcement learning algorithm, takes around 8x more time to train than PtrNetDecoding with a similar GPU configuration. The speedup and smaller memory consumption will be useful when we move from sentence-level extraction to document-level extraction, since document length is much higher than sentence length and a document contains a higher number of tuples.
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